WWP: Old fight, new tricks?

One of the most interesting papers I saw at the ASSA meetings in January was Ariel Ortiz-Bobea's new work on the climate and agriculture question. For anyone not in the know, there is a long (read: loooooooong) literature trying to estimate the effects climate change will have on agriculture. Most of this debate has focused on the US, largely for data reasons (and partly because US maize is way sexier than Kenyan maize...amirite?).  

An overly brief summary of this literature is the following:

• In the Beginning, agronomists created the Crop Model. These models were created using test plots, and used to predict the effects of climate on agriculture.
• Then, some economists came along, and made the point that the agronomists were selling the farmers short. Crop models ignore the potential for farmer adaptation. And thus the Ricardian model was born: these economists regress land values on average temperatures, plus a bunch of controls, and find mild-to-positive effects of climate change. 
• But wait! Enter Team ARE. A second set of economists argued that the Ricardian approach, like most cross-sectional regressions, suffered from omitted variable bias. In particular, they note that the presence of irrigation dramatically changes agriculture, and suggested estimating different models for irrigated and non-irrigated regions (if you're keeping score at home, you can also implement this suggestion via an interacted model). When they account for irrigation, climate change looks pretty bad again.
• A few years later, some other economists arrived on the scene. If you're worried about irrigation, they argued, you should be worried about a whole host of other omitted variables in the cross-section. But do we have the idea for you? These guys used a panel fixed effects model to remove time-invariant omitted variables - also sparking a debate about "weather vs. climate" (using short-run fluctuations rather than long-run variation to estimate the model in question) - and find again that climate change probably isn't so bad.
• Unnnnnfortunately, our panel-data-wielding heroes had some data problems (brought to light by Team ARE). If you correct them, climate change harms US agriculture to the tune of tens of billions of dollars. Oops.
• But the weather-vs-climate thing is still unsatisfying! So Team ARE: The Next Generation used a long-difference estimator to show that actually, farmers don't seem to be doing a better job responding to climate change over time - it'll still be bad.

Here's where Ariel's new paper comes along. He notes that (for various reasons glossed over above) we might actually want to run a Ricardian-style model: in essence, weather vs. climate hasn't been fully resolved. At the same time, though, we should be worried about omitted variable bias. But in particular, we should be worried about spatially-dependent omitted variable bias. The argument is pretty simple. Most things that might be left out of an agriculture-climate regression that would bias that regression vary smoothly over space. Conveniently for the econometrician, there are some newer estimators that we can use to understand the magnitude and direction of the bias that might result from these types of omitted variables. Ariel uses these tricks, and finds that (lo and behold) climate change might not be so bad for agriculture in the US after all.

Effects of climate change estimated using OLS: this is the original economist version.

New-fangled effects of climate change using the Spatial Durbin Model. Note the lack of hugely negative effects, especially towards the right of the figure.

This paper is full of technical details, makes some fairly strong structural assumptions about exactly how omitted variables vary over space, and ends up with fairly wide confidence intervals, but all in all, it makes a useful contribution to an important debate, and is worth a read. I'll be interested to see where it ends up, and how seriously the literature takes the re-posited suggestion that climate change really isn't that bad for US ag. If nothing else, this paper highlights just how important it is for us to figure out how to measure adaptation!

Bonus: If you've read this far down, you deserve something fun. Go check out my new favorite internet game. h/t Susanna & Paula.

Edited to fix links. Thanks to my usual blog-police for pointing this out.

No, really, climate change will be bad!

I would be remiss to purport to blog about the economics of energy, the environment, and the developing world if I failed to highlight a new (important) study that came out in Nature this week.

The all-star team of Marshall Burke, Sol Hsiang (who has a fancy new website), and Ted Miguel is at it again, with a paper on the effects of temperature on GDP around the world. Before they even get to the empirics, they provide some really nice insight as to why when there are sharp non-linearities in micro temperature response functions, we shouldn't expect to see these same kinks in macro response functions. The idea is basically the following: a micro response function tells us the marginal effect of having an additional (hour, day) in a given temperature range. Imagine, as with US maize and lots of other things, temperature is increasing up to a point and then has a sharp decrease beyond that point. The macro response function will aggregate these days or hours up to a longer time period (a year, say), meaning that the overall effect of annual temperature on annual output will be a weighted average of the two slopes of the micro response, weighted by the number of days in each temperature range. Was that confusing? Check out Figure 1, panels d, e, and f (the math to derive this is all in the supplement to the paper as well):

Source: Burke, Hsiang, Miguel (Nature 2015).

This key insight is really important in allowing us to understand how we should expect micro responses to differ from macro ones. Cool. 

The authors then go on to empirically estimate the global macro temperature response function, settling on (after many robustness checks) a quadratic in temperature. What they come up with is a strong inverted-u shaped relationship, with an optimum around 55F (that might seem low, but remember that we're talking about annual average temperature here). This suggests that some (colder) countries might benefit from global warming, and hotter countries have a lot to lose. They tackle several points that are often brought up in this literature, and end up unable to reject that the rich and poor country responses are the same (though the confidence intervals are quite large as well. Minor gripe: 90% confidence intervals are shown in the paper. Yes, I know that 95% is arbitrary too, but it is the empirical economics standard...); they show that agriculture takes a big hit in both poor and rich countries, and that non-ag GDP seems to take slightly less of one in richer countries, but the relationship between temperature and non-ag GDP is still downward sloping; and finally, that the response functions in 1960-1989 look almost identical to the 1990-2010 response functions, suggesting that there hasn't been a ton of adaptation during the time period of their data.

Using these estimates, they go on to make some beautiful figures showing climate damage projections out to 2100 (IMHO, as much as I know that they like Figure 3, I think it's aesthetically pleasing but not the most legible). They find that, using fairly reasonable assumptions about growth and emissions paths, global GDP is projected to be approximately 25% lower in 2100 with climate change than without -- a much larger effect than all three current IAMs used in US policy (DICE, FUND, and PAGE) would suggest . There are wide confidence intervals around this estimate to be sure - but it's also worth noting that the majority of the uncertainty here comes from Europe and North America. These are large economies, and so have a large effect on GDP per capita overall, but are also close to the estimated global optimum, meaning that if the optimum is off by a little bit, the effects for these countries could even flip in sign.

I think this paper is a really important contribution to the climate-economics space. The effects are huge, and the paper (and supplemental information, and stuff that got left out of the supplemental information but was in an earlier non-circulating working paper version) is very thorough.

A few small comments: it is worth noting that there's a ton of statistical uncertainty floating around here.  Panel C of the first extended data figure shows the estimated marginal effects with lags included - and in every estimate that includes lags, the confidence interval bounds zero (and I think these are still the 90% CI's?). The confidence intervals on Figure 5a, the main estimate, also sit squarely on top of zero. And, as with every projection exercise, we should take this one with a giant brick of salt. These guys do a good job, but remember that they're also using short-run fluctuations in temperatures to trace out this response function. This is nice because, conditional  on the right fixed effects, we generally think that it's as good as randomly assigned, but does make plugging the estimates into a projection a little tricky to interpret. It's standard in this literature to do this kind of thing - and the fact that they find no evidence of adaptation in the 50+ year period they're looking at helps shore up the argument for doing so - but it's worth keeping in mind that that's what's being done.

It's also really important to think carefully (in all of these papers - not just BHM) about what's actually being used for identification. We know from Wolfram and Craig McIntosh that using higher-order polynomials in fixed effects models re-introduces cross sectional variation (and any omitted variable bias that comes with it!). I think in an earlier version of the paper, I saw a binned model floating around, which removes this concern, and had similar point estimates, but this general point is something that's under-appreciated, I think. (And, even with binned models, we need to be really careful when presenting something as the aggregate temperature response function, if there are only a few countries that ever end up in the really hot bins. That's a soapbox for another day.)

Also, as I mentioned above a little bit, while it's true that these guys aren't able to statistically reject that the poor and rich country responses are different, that doesn't mean that the true responses aren't different - it could be that there's not enough statistical power to address these questions in the data. That's going to be especially true at the colder end of the distribution - there are so few poor countries there that it's really hard to say anything concrete. 

All that said, I think this is a super interesting and important paper, and I'm glad that it's out in time for Paris. I've already learned a lot from these guys, and I continue to do so - they're some of the most careful, thorough, and productive researchers out there working on really policy-relevant topics. Plus, they make beautiful figures. This is a paper that's really worth diving into - I highly recommend actually reading the paper, the extended data, and the supplemental information (which is something I won't say very often)!

One last thing before I close: Marshall, Sol, and Ted have put up a really good companion site to their paper, that makes the results accessible and digestible. Plus, they've put up replication code - very important when you're working on such hot (ha) issues as climate and GDP. Take a look!

Edited to add: Marshall just posted a response to some frequent criticism on his blog. Worth a read.

Aaaand we're back!

Married and everything. Couldn't have asked for a better wedding, nor for a better trip to New Orleans! 

To get back in the swing of things a little bit (no WWP from this weekend because I was happily banned from doing work): because my PhD (if and when I eventually get it) will say "Agricultural" on it, I guess I should occasionally post something about agriculture. To that end, a few quick things to highlight:

Interesting that this is the first picture that comes up when I Google-image search "agriculture." I was expecting US maize (which, to be fair, was the next hit...). Source.

• Mike Roberts' has a compelling blog post which was inspired by Angus Deaton's Nobel Prize victory (which, of course, happened while I was away - but it's great to see a development economist with a bent towards empirics being awarded the Prize). Mike (an ARE grad himself, no less) has a nice little piece about Deaton's work on commodity prices, and the value of fully thinking through the implications of estimated empirical results.
• Planet Money continues its run of excellent episodes with a cool discussion of futures markets. Definitely worth a listen.
• Finally, (not directly ag) Max Auffhammer points out that this new project "has famous econometricians in it."

Also, in totally non-economics-related news, I couldn't finish this blog post without highlighting my alma mater's excellent performances at the Head of the Charles this weekend. The men came 3rd, for their best placing since 2011, and the women (for whom I coxed while I was there) won for the first time since my senior year. Awesome. 

Friday links: Night lights, monsoons, and guns.

I'm going to be away on my honeymoon next week, so no blog posts (by order of the totally-has-her-priorities-in-the-right-place fiancee). I'll leave you with one extra post this week, featuring some interesting tidbits from around the web.

India's border with Pakistan utilizes enough lighting to be seen from space.

Early map of the monsoon advance (from here).

India's weather forecasters did a much-better-than-usual job (take that, Quartz!) of forecasting the monsoon this year. (Though it's a little hard to know whether this was luck or actual improvements in the forecasting process, as the government suggests.) Getting these forecasts right is crucial: take a look at this really neat paper by Mark Rosenzweig and Chris Udry (two of my favorite authors in development economics) at Yale for reasons why.

Planet Money featured Berkeley economist David Card on the current influx of migrants to Europe. His results from Miami in the 1980s suggest that many peoples' fears about immigrants are unfounded.

A new paper in October's AEJ:Applied has a depressing title.

Economist Ryan Briggs has a moving piece on what development means in the wake of his newborn son's illness.

Cool maps (with really ugly color schemes) about the spatial organization of cities.

Trevor Noah has three great pieces: two on guns, and another on the one and only D. Trump.

Also, the Springboks are officially moving into the quarter finals of the Rugby World Cup (and Bryan Habana is now the all-time RWC leader in tries scored)!

Wednesday websites

It's been a little bit of a crazy beginning of this week - I've finally scheduled my oral exam, I have a big RA task at the moment, and I'm getting married in less than 3 weeks! All of this is contributing to a lack of a thorough, thoughtful blog post this week (but hey - as a wise friend of mine once said, "Excuses are the bricks that build the house of failure.") In lieu of an actual post, here are some things that have popped up on my internet-radar this week (if it's good enough for Chris Blattman, it's good enough for me!)

A clear, well-done video about Syrian refugees. (h/t Erin)

A fascinating National Geographic article about traveling the Congo river.

VW epically cheated on its emissions testing. Vox explains. (As does Max.)

Buzzfeed has a quiz every climate nerd will enjoy. (h/t Sol)

Enjoy!